Pre-processing: During training on Aircraft, CUB, mini-ImageNet and tiered-ImageNet (data from DeepEMD [1]}'s implementationhttps://github.com/icoz69/DeepEMD), images are randomly cropped and then resized into 84\(\times \) 84i.e., torchvision.transforms.RandomResizedCrop(84). For meta-iNat, tiered meta-iNat, and tiered-ImageNet using the original release by Ren  [2]}https://github.com/renmengye/few-shot-ssl-public, images are randomly padded then cropped into 84\(\times \) 84i.e., torchvision.transforms.RandomCrop(84, padding=8). Then all images are augmented by color-jittering and random horizontal flipping.
